{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food%</th>\n",
       "      <th>Fresh%</th>\n",
       "      <th>Drinks%</th>\n",
       "      <th>Home%</th>\n",
       "      <th>Beauty%</th>\n",
       "      <th>Health%</th>\n",
       "      <th>Baby%</th>\n",
       "      <th>Pets%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>9.46</td>\n",
       "      <td>87.06</td>\n",
       "      <td>3.48</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>15.87</td>\n",
       "      <td>75.80</td>\n",
       "      <td>6.22</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>16.88</td>\n",
       "      <td>56.75</td>\n",
       "      <td>3.37</td>\n",
       "      <td>16.48</td>\n",
       "      <td>6.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>28.81</td>\n",
       "      <td>35.99</td>\n",
       "      <td>11.78</td>\n",
       "      <td>4.62</td>\n",
       "      <td>2.87</td>\n",
       "      <td>15.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24.13</td>\n",
       "      <td>60.38</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.72</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29995</td>\n",
       "      <td>5.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29996</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29997</td>\n",
       "      <td>9.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77.48</td>\n",
       "      <td>13.27</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29998</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Food%  Fresh%  Drinks%  Home%  Beauty%  Health%  Baby%  Pets%\n",
       "0       9.46   87.06     3.48   0.00     0.00     0.00    0.0    0.0\n",
       "1      15.87   75.80     6.22   2.12     0.00     0.00    0.0    0.0\n",
       "2      16.88   56.75     3.37  16.48     6.53     0.00    0.0    0.0\n",
       "3      28.81   35.99    11.78   4.62     2.87    15.92    0.0    0.0\n",
       "4      24.13   60.38     7.78   7.72     0.00     0.00    0.0    0.0\n",
       "...      ...     ...      ...    ...      ...      ...    ...    ...\n",
       "29995   5.80    0.00    51.30   0.00     0.00     0.00    0.0   42.9\n",
       "29996   0.00    0.00     0.00   0.00   100.00     0.00    0.0    0.0\n",
       "29997   9.25    0.00    77.48  13.27     0.00     0.00    0.0    0.0\n",
       "29998   0.00    0.00   100.00   0.00     0.00     0.00    0.0    0.0\n",
       "29999   0.00    0.00     0.00   0.00     0.00     0.00  100.0    0.0\n",
       "\n",
       "[30000 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"dataset/order.csv\", header=0)\n",
    "# data.drop([\"Id\"], axis=1, inplace=True)\n",
    "t = data.iloc[:,-8:]\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KMeans:\n",
    "    '''Kmeans聚类算法实现'''\n",
    "    def __init__(self, k, times):\n",
    "        '''初始化\n",
    "        \n",
    "        Parameters\n",
    "        -----\n",
    "        k: int 聚成几个类\n",
    "        times: int 迭代次数\n",
    "        '''\n",
    "        self.k = k\n",
    "        self.times = times\n",
    "    def fit(self, X):\n",
    "        '''根据所给数据训练\n",
    "        \n",
    "        Pararmeters\n",
    "        ------\n",
    "        X: 类数组类型，形如：[样本数量，特征数量]\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        # 设置随机数种子，以便于可以相同的随机系列，以便随机结果重现\n",
    "        np.random.seed(0)\n",
    "        # 从数组中随机选择K个点作为初始聚类中心\n",
    "        self.cluster_centers_ = X[np.random.randint(0, len(X), self.k)]\n",
    "        # 用于存放数据所属标签\n",
    "        self.labels_ = np.zeros(len(X))\n",
    "        # 开始迭代\n",
    "        for t in tqdm(range(self.times)):\n",
    "            # 循环遍历样本计算每个样本与聚类中心的距离\n",
    "            for index,x in enumerate(X):\n",
    "                # 计算每个样本与每个聚类中心的欧式距离\n",
    "                dis = np.sqrt(np.sum((x - self.cluster_centers_)**2, axis=1))\n",
    "                # 将最小距离的索引赋值给标签数组，索引的值就是当前所属的簇。范围威威（0，K-1）\n",
    "                self.labels_[index] = dis.argmin()\n",
    "            # 循环便利每一个数更新聚类中心\n",
    "            for i in range(self.k):\n",
    "                # 计算每个簇内所有点的均值，用来更新聚类中心\n",
    "                self.cluster_centers_[i] = np.mean(X[self.labels_==i], axis=0)\n",
    "    def predict(self, X):        \n",
    "        '''预测样本属于哪个簇\n",
    "\n",
    "        Parameters\n",
    "        -----\n",
    "        x: 类数组类型。形如[样本数量。特征数量]\n",
    "\n",
    "        Reeturn\n",
    "        -----\n",
    "        result: 类数组，每一个x所属的簇\n",
    "        '''\n",
    "        X = np.asarray(X)\n",
    "        result = np.zeros(len(X))\n",
    "        for index,x in enumerate(X):\n",
    "            # 计算样本与聚类中心的距离\n",
    "            dis = np.sqrt(np.sum((x - self.cluster_centers_)**2, axis=1))\n",
    "            # 找到距离最近的聚类中中心划分一个类别\n",
    "            result[index] = dis.argmin()\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:43<00:00,  1.15it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[46.33977936,  8.93380516, 23.19047005, 13.11741633,  4.8107557 ,\n",
       "         1.17283735,  1.35704647,  0.95392773],\n",
       "       [19.5308009 , 50.42856608, 14.70652695,  7.89437019,  3.69829234,\n",
       "         0.91000428,  1.92515077,  0.82113238],\n",
       "       [ 7.93541008,  4.56182052, 30.65583437, 18.57726789,  8.61597195,\n",
       "         1.28482514, 26.81950293,  1.30158264]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(3, 50)\n",
    "kmeans.fit(t)\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food%</th>\n",
       "      <th>Fresh%</th>\n",
       "      <th>Drinks%</th>\n",
       "      <th>Home%</th>\n",
       "      <th>Beauty%</th>\n",
       "      <th>Health%</th>\n",
       "      <th>Baby%</th>\n",
       "      <th>Pets%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>48.23</td>\n",
       "      <td>20.37</td>\n",
       "      <td>15.38</td>\n",
       "      <td>8.29</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>24.10</td>\n",
       "      <td>22.29</td>\n",
       "      <td>38.69</td>\n",
       "      <td>14.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>36.51</td>\n",
       "      <td>31.93</td>\n",
       "      <td>27.18</td>\n",
       "      <td>4.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>22.76</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>77.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>65.64</td>\n",
       "      <td>12.36</td>\n",
       "      <td>21.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29974</td>\n",
       "      <td>33.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.46</td>\n",
       "      <td>41.46</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29977</td>\n",
       "      <td>45.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>26.68</td>\n",
       "      <td>28.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29988</td>\n",
       "      <td>28.21</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.34</td>\n",
       "      <td>23.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29989</td>\n",
       "      <td>61.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.34</td>\n",
       "      <td>15.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29990</td>\n",
       "      <td>29.74</td>\n",
       "      <td>28.72</td>\n",
       "      <td>19.52</td>\n",
       "      <td>22.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9382 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Food%  Fresh%  Drinks%  Home%  Beauty%  Health%  Baby%  Pets%\n",
       "15     48.23   20.37    15.38   8.29     7.73      0.0    0.0    0.0\n",
       "23     24.10   22.29    38.69  14.92     0.00      0.0    0.0    0.0\n",
       "24     36.51   31.93    27.18   4.38     0.00      0.0    0.0    0.0\n",
       "40     22.76    0.00     0.00  77.24     0.00      0.0    0.0    0.0\n",
       "43     65.64   12.36    21.99   0.00     0.00      0.0    0.0    0.0\n",
       "...      ...     ...      ...    ...      ...      ...    ...    ...\n",
       "29974  33.93    0.00    17.46  41.46     7.15      0.0    0.0    0.0\n",
       "29977  45.10    0.00    26.68  28.22     0.00      0.0    0.0    0.0\n",
       "29988  28.21    0.00    48.34  23.44     0.00      0.0    0.0    0.0\n",
       "29989  61.32    0.00    23.34  15.34     0.00      0.0    0.0    0.0\n",
       "29990  29.74   28.72    19.52  22.02     0.00      0.0    0.0    0.0\n",
       "\n",
       "[9382 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看某个簇内的所有样本数据\n",
    "t[kmeans.labels_==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 2., 1.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.predict([[30,30,40,0,0,0,0,0],[0,0,0,0,0,30,30,40],[30,30,0,0,0,0,20,20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 50/50 [00:43<00:00,  1.15it/s]\n"
     ]
    }
   ],
   "source": [
    "t2 = data.loc[:,\"Food%\":\"Fresh%\"] # 需要注意loc函数是用字符作为索引的。并且包含:后面的那一列\n",
    "kmeans = KMeans(3, 50)\n",
    "kmeans.fit(t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rcParams[\"font.family\"] = \"SimHei\"\n",
    "mpl.rcParams[\"axes.unicode_minus\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "# 绘制每个类别散点图\n",
    "plt.scatter(t2[kmeans.labels_==0].iloc[:,0], t2[kmeans.labels_==0].iloc[:,1], label=\"类别1\")\n",
    "plt.scatter(t2[kmeans.labels_==1].iloc[:,0], t2[kmeans.labels_==1].iloc[:,1], label=\"类别1\")\n",
    "plt.scatter(t2[kmeans.labels_==2].iloc[:,0], t2[kmeans.labels_==2].iloc[:,1], label=\"类别1\")\n",
    "# 绘制聚类中心\n",
    "plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1], marker=\"+\",s=300)\n",
    "plt.title(\"食物与肉类购买聚类分析\")\n",
    "plt.xlabel(\"食物\")\n",
    "plt.ylabel(\"肉类\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
